Aviz Networks Blogs

The AI network doesn't just begin when the first GPU is plugged into a switch. A day zero in NVIDIA Spectrum-X involves topology definition, IP assignment, BGP setup, QoS settings, etc. At scale, the human effort that goes into configuring all of these components can turn into a substantial engineering effort. Intent-based automation can alleviate these difficulties. Starting with basic requirements such as Scale Unit and number of GPUs, IP ranges to utilize, Aviz ONES Fabric Designer can generate both the design of the fabric and configuration for the desired environment. This takes care of things on both ends of the system, from the IPCLOS underlay (leaf and spine setup, BGP configuration, ASN assignment, IP address, interfaces and MTU setup) all the way up to server-side setup (RoCEv2 parameters with PFC, and ECN, BlueField-3 settings, etc.). But setting up is only one hurdle. Validation is the other, so using NVIDIA AIR enables validation of the design, before rolling it out in...

Your AI network design needs more than just GPUs. Did you know that the Day 0 design decisions, topology, IP addressing, BGP, QoS, server connectivity, and more can have a dramatic impact on the performance of your network Fabric in production? Designing and deploying high-performance AI networking at scale using, for example, NVIDIA Spectrum-X is challenging. You can simplify Day 0 design and deployment with Aviz ONES Fabric Designer. Instead of hand-configure complex IPCLOS Fabrics and dealing with hundreds or even thousands of parameters, the ONES Fabric Designer lets teams input data at a high level – specifying the Scalable Unit (SU) size, the desired number of GPUs, and the IP address pool. The system then generates a tested and validated network Fabric design including spine and leaf configuration, BGP peering, interface settings, and IP assignments. Furthermore, the solution also fully automates the setup and configuration of NVIDIA BlueField and ConnectX SuperNICs including a...

Until recently, companies sold innovative hardware. Now companies are focused more on integrated and proprietary software. AI is disrupting companies' old ways. Quickly upgradable chips and networks are no longer the most important hardware to own. Long term value comes from data, models, and workflows that utilize AI to run applications. The biggest challenge to AI businesses is: Fast-developing hardware generations. Hard-to-keep-up-with upgrades in infrastructure. Dependence on hardware for developing applications. Growing demand for deploying AI quicker at a larger scale. The answer is not decreasing hardware spending. The solution is spending to develop hardware that is more flexible and evolving. The adaptability of infrastructure allows organizations to upgrade hardware without the difficulty of rebuilding applications. Integrating upgrades in hardware allows organizations to keep their AI workflows running and evolving. Implementing operating standards is what the market is ...

The way data center networks operate is changing with the evolving nature of AI workloads. In a recent Aviz Networks podcast, Taylor Allison, Senior Product Marketing Manager at NVIDIA, spoke about changes that will happen in networking when it comes to large-scale AI training and inference. To do AI training, GPUs need to work in tandem, which requires a network that is able to communicate with low latency and stay synchronized. Once you increase the number of GPUs, even more, the existing networking technologies will start to fail. The networking solutions from NVIDIA that are optimized for AI workloads include Spectrum-X built for AI clusters over Ethernet and InfiniBand, another high-performance networking architecture for AI overclusters. The podcast also covered NVIDIA Air and the digital twin technology. Before deploying configurations and automation to the production environments, users can create virtual environments in which they can test configurations and automation. This g...

To investigate cyberattacks and suspicious activity - and to meet compliance regulations - security teams rely on evidence found within packets. However, traffic is traversing not only across their data centers, but also cloud platforms, campuses, and edges, making it challenging to capture every essential packet. Some typical problems that teams encounter are: * Overloaded packet capture systems unable to handle traffic spikes. * Irrelevant data obscuring important evidence. * Inconsistent historical records creating drag on investigations. * Higher infrastructure costs, without an increase in visibility. The Aviz Networks-Endace integrated solution makes this task simpler by guaranteeing that only optimized, high-fidelity traffic arrives at packet capture systems. Aviz Deep Network Observability (DNO) smart services efficiently aggregate, filter, dedup, and normalize traffic before directing it to Endace Always-On Packet Capture, which ingests, indexes, and permanently stores the tra...

Assembling AI infrastructure is significantly different than just installing servers and switches. Modern AI factories are highly interconnected and need to integrate networking, GPUs, storage, orchestration, and suites of applications. If configuration or integration issues appear in the deployed system, there will likely be major interruptions in the operation and costs associated with delays. Many companies struggle with: * A shortage of real-world physical lab testing. * Long validation/deployment cycles. * Discovering integration bugs at an inconvenient (read, costly!) late stage. * The hidden post-deployment rework needed to fix. That's where Aviz ONES on NVIDIA DSX Air helps deliver a better AI infrastructure build process: digital twin simulation. The idea is simple: instead of building, and then testing, why not design, simulate, and validate your entire AI networking environment beforehand? Engineers can create a model of their production-scale network, simulate traffic f...